To show that the improved performance of our model is independent of the layout representation,
we train a model on the same `tight' room layout representation as HEAT and report the scores in Tab. REF  marked with a star (\(^*\) ).
Note again that all models are evaluated on the same non-processed groundtruth annotations of Structured3D [1]}.
We observe that the room metrics of the model trained on tight room layouts drop a bit compared with our original model, while still outperforming all other methods.
The drop in scores is not surprising since the modified training data is only an approximation of the original groundtruth used for evaluation.
Furthermore, the room metrics penalize overlap between rooms [2]}.
Results from tight room layouts are more likely to overlap, which can potentially degrade the room metrics.
Interestingly, the angle metrics improve, especially when it comes to angle precision, which already outperforms MonteFloor.
In the tight room layout, we encourage corners in adjacent walls to have exactly complementary angles.
This implicit constraint might help the model to reason about angle relationships,
thus benefiting the angle metrics.
More qualitative results can be found in Fig. REF .
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